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@ -1,334 +0,0 @@
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import numpy as np
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import cv2
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import matplotlib.pyplot as plt
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from matplotlib import font_manager
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import ddddocr
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import threadsafe_tkinter as tk
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from tkinter import filedialog
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font_path = "C:/Users/Lenovo/Downloads/OPPOSans3.0/OPPOSans-Regular.ttf"
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# 动态添加字体到 matplotlib 的字体列表
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font = font_manager.FontProperties(fname=font_path)
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class Get_license():
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#图像拉伸函数
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def stretch(self, img):
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maxi = float(img.max())
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mini = float(img.min())
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for i in range(img.shape[0]):
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for j in range(img.shape[1]):
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img[i, j] = (255 / (maxi - mini) * img[i, j] - (255 * mini) / (maxi - mini))
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return img
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#二值化处理函数
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def dobinaryzation(self, img):
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# 使用OpenCV的自动阈值方法
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ret, thresh = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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return thresh
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#寻找矩形的轮廓
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def find_rectangle(self, contour):
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y, x = [],[]
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for p in contour:
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y.append(p[0][0])
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x.append(p[0][1])
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return [min(y), min(x), max(y), max(x)]
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#定位车牌号
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def locate_license(self, img, afterimg):
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contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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#找出最大的三个区域
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block = []
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for c in contours:
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#找出轮廓的左上点和右下点
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#由此计算它的面积和长度比
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r = self.find_rectangle(c)
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a = (r[2] - r[0]) * (r[3] - r[1]) #面积
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s = (r[2] - r[0]) * (r[3] - r[1]) #长度比
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block.append([r, a, s])
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#选出面积最大的3个区域
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block = sorted(block, key=lambda b: b[1])[-3:]
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#使用颜色识别判断找出最像车牌的区域
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maxweight, maxindex = 0, -1
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for i in range(len(block)):
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b = afterimg[block[i][0][1]:block[i][0][3], block[i][0][0]:block[i][0][2]]
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hsv = cv2.cvtColor(b, cv2.COLOR_BGR2HSV)
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lower = np.array([100, 50, 50])
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upper = np.array([140, 255, 255])
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mask = cv2.inRange(hsv, lower, upper)
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# 计算mask中白色像素（即HSV范围内的像素）的数量
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weight = np.sum(mask == 255) # 或者简单地使用 np.count_nonzero(mask)
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# 选出最大权值的区域
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if weight > maxweight:
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maxindex = i
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maxweight = weight
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return block[maxindex][0]
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#预处理函数
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def find_license(self, img):
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m = 400 * img.shape[0] / img.shape[1]
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#压缩图像
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img = cv2.resize(img, (400, int(m)), interpolation=cv2.INTER_CUBIC)
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#BGR转换为灰度图像
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gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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#灰度拉伸
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stretchedimg = self.stretch(gray_img)
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'''进行开运算，用来去除噪声'''
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r = 16
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h = w = r * 2 + 1
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kernel = np.zeros((h, w), np.uint8)
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cv2.circle(kernel, (r, r), r, 1, -1)
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openingimg = cv2.morphologyEx(stretchedimg, cv2.MORPH_OPEN, kernel)
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strtimg = cv2.absdiff(stretchedimg, openingimg)
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#图像二值化
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binaryimg = self.dobinaryzation(strtimg)
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#canny边缘检测
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canny = cv2.Canny(binaryimg, binaryimg.shape[0], binaryimg.shape[1])
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'''消除小的区域，保留大块的区域，从而定位车牌'''
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#进行闭运算
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kernel = np.ones((5, 17), np.uint8)
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closingimg = cv2.morphologyEx(canny, cv2.MORPH_CLOSE, kernel)
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#进行开运算
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openingimg = cv2.morphologyEx(closingimg, cv2.MORPH_OPEN, kernel)
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#再次进行开运算
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#kernel = np.ones((5, 5), np.uint8)
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#openingimg=cv2.morphologyEx(openingimg,cv2.MORPH_CLOSE,kernel)
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#openingimg = cv2.morphologyEx(openingimg, cv2.MORPH_OPEN, kernel)
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#消除小区域，定位车牌位置
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rect = self.locate_license(openingimg, img)
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return rect, img
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#图像分割函数
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def cut_license(self, afterimg, rect):
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#转换为宽度和高度
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rect[2] = rect[2] - rect[0]
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rect[3] = rect[3] - rect[1]
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rect_copy = tuple(rect.copy())
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#创建掩膜
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mask = np.zeros(afterimg.shape[:2], np.uint8)
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#创建背景模型 大小只能为13*5，行数只能为1，单通道浮点型
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bgdModel = np.zeros((1, 65), np.float64)
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#创建前景模型
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fgdModel = np.zeros((1, 65), np.float64)
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#分割图像
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cv2.grabCut(afterimg, mask, rect_copy, bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_RECT)
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mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
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img_show = afterimg * mask2[:, :, np.newaxis]
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return img_show
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class Segmentation():
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def __init__(self, cutimg):
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#1、读取图像，并把图像转换为灰度图像并显示
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img_gray = cv2.cvtColor(cutimg, cv2.COLOR_BGR2GRAY) #转换了灰度化
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#2、将灰度图像二值化，设定阈值是100
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self.img_thre = img_gray
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cv2.threshold(img_gray, 150
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, 255, cv2.THRESH_BINARY_INV, self.img_thre)
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#cv2.waitKey(0)
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#3、保存黑白图片
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cv2.imwrite('thre_res.jpg', self.img_thre)
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#4、分割字符
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self.white = [] #记录每一列的白色像素总和
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self.black = [] #黑色
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self.height = self.img_thre.shape[0]
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self.width = self.img_thre.shape[1]
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self.white_max = 0
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self.black_max = 0
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#计算每一列的黑白色像素总和
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for i in range(self.width):
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white_count = 0 #这一列白色总数
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black_count = 0 #这一列黑色总数
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for j in range(self.height):
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if self.img_thre[j][i] == 255:
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white_count += 1
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if self.img_thre[j][i] == 0:
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black_count += 1
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self.white_max = max(self.white_max, white_count)
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self.black_max = max(self.black_max, black_count)
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self.white.append(white_count)
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self.black.append(black_count)
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self.arg = False #False表示白底黑字；True表示黑底白字
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if self.black_max > self.white_max:
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self.arg = True
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def heibai(self):
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return self.img_thre
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def find_end(self, start_):
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end_ = start_ + 1
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for m in range(start_ + 1, self.width - 1):
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if (self.black[m] if self.arg else self.white[m]) > (
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0.85 * self.black_max if self.arg else 0.85 * self.white_max):
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end_ = m
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break
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return end_
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def display(self):
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#img_list = []
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n = 1
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plt.figure()
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img_num = 0
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while n < self.width - 2:
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n += 1
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if (self.white[n] if self.arg else self.black[n]) > (
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0.15 * self.white_max if self.arg else 0.15 * self.black_max):
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#上面这些判断用来辨别是白底黑字还是黑底白字
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start = n
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end = self.find_end(start)
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n = end
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if end - start > 5:
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cj = self.img_thre[1:self.height, start:end]
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img_num += 1
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cj = cv2.cvtColor(cj, cv2.COLOR_RGB2BGR)
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plt.figure(2)
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plt.subplot(2, 4, img_num)
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plt.title('{}'.format(img_num))
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plt.imshow(cj)
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plt.show()
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return self.img_thre
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if __name__ == '__main__':
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def select_image():
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# 弹出文件选择对话框，限制文件类型为图片
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file_path = filedialog.askopenfilename()
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if file_path:
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# 将文件路径显示在标签上
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img = cv2.imread(file_path)
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img1 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
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# 绘图
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plt.figure(1)
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plt.suptitle('车牌识别', fontproperties=font)
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plt.subplot(2, 3, 1)
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plt.title('原始图像', fontproperties=font)
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plt.imshow(img1)
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# 预处理图像
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license = Get_license()
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rect, afterimg = license.find_license(img)
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afterimg = cv2.cvtColor(afterimg, cv2.COLOR_RGB2BGR)
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plt.subplot(2, 3, 2)
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plt.title('预处理后图像', fontproperties=font)
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plt.imshow(afterimg)
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# 车牌号打框
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cv2.rectangle(afterimg, (rect[0], rect[1]), (rect[2], rect[3]), (0, 255, 0), 1)
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x1, y1, x2, y2 = int(rect[0]), int(rect[1]), int(rect[2]), int(rect[3])
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plt.subplot(2, 3, 3)
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plt.title('车牌框出', fontproperties=font)
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plt.imshow(afterimg)
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# 背景去除
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cutimg = license.cut_license(afterimg, rect)
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plt.subplot(2, 3, 4)
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plt.title('车牌背景去除', fontproperties=font)
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plt.imshow(cutimg)
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# print(int(_rect[0]), int(_rect[3]), int(_rect[2]), int(_rect[1]))
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# 开始分割车牌
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# cutimg = cutimg[140:165, 151:240]
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cutimg = cutimg[y1 + 3:y2 - 3, x1 - 1:x2 - 3]
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# cutimg = cutimg[int(_rect[0]):int(_rect[3]),int(_rect[2]):int(_rect[1])]
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height, width = cutimg.shape[:2]
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cutimg1 = cv2.resize(cutimg, (2 * width, 2 * height), interpolation=cv2.INTER_CUBIC)
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plt.subplot(2, 3, 5)
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plt.title('分割车牌与背景', fontproperties=font)
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plt.imshow(cutimg)
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# 字符切割
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seg = Segmentation(cutimg)
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plt.subplot(2, 3, 6)
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img_hei = seg.heibai()
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img_hei = cv2.cvtColor(img_hei, cv2.COLOR_RGB2BGR)
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plt.title('车牌二值化处理', fontproperties=font)
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plt.imshow(img_hei)
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seg.display()
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plt.show()
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# 打印车牌
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ocr = ddddocr.DdddOcr()
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with open('thre_res.jpg', 'rb') as f:
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image = f.read()
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res = ocr.classification(image)
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print(res)
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# 创建根窗口
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root = tk.Tk()
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root.title('车牌识别程序')
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root.geometry('600x450')
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# 创建一个标签用于显示图片路径
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label = tk.Label(root, text='未选择图片')
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label.pack(pady=20) # 使用pack布局管理器，并添加一些垂直填充
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def exit_app():
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# 退出应用程序的函数
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root.destroy()
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# 创建一个按钮，点击时会调用select_image函数
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button = tk.Button(root, text='选择图片', command=select_image)
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button.pack()
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button_exit = tk.Button(root, text='退出', command=exit_app)
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button_exit.pack(pady=30)
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# 启动事件循环
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root.mainloop()
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